We examine a model that focuses on forming an information cascade of user comments on a post within an online social network. These comments exhibit various attitudes toward a specific issue and can be categorized as positive, negative, or neutral. The probability of a user composing a comment with a particular attitude is influenced by their initial opinion and the collection of previously posted comments they encounter. The arrangement of these comments in a specific order is determined by the social network's algorithm. Through simulation modeling, this study investigates the impact of the algorithm on the properties of the resulting information cascade.